Final Project

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install.packages('sf')

Error in install.packages : Updating loaded packages

install.packages('raster')

Error in install.packages : Updating loaded packages

install.packages('dplyr')

Error in install.packages : Updating loaded packages

install.packages('spData')

Error in install.packages : Updating loaded packages

install.packages('spDataLarge')

Installing package into 㤼㸱C:/Users/WHH/Documents/R/win-library/3.5㤼㸲
(as 㤼㸱lib㤼㸲 is unspecified)

Warning in install.packages :
  package ‘spDataLarge’ is not available (for R version 3.5.2)

library(sf)
library(raster)
library(dplyr)
library(spData)
library(spDataLarge)
install.packages("spDataLarge", repos = "https://nowosad.github.io/drat/", type = "source")

Error in install.packages : Updating loaded packages

library(tmap)
library(leaflet)
library(mapview)
library(ggplot2)
library(shiny)

map_tract <- tm_shape(tract,name = 'X2018_Murde') + tm_polygons('X2018_Murde')+tm_shape(tract,name = 'X2018_Motor') + tm_polygons('X2018_Motor')+tm_shape(tract,name = 'X2018_Larce') + tm_polygons('X2018_Larce')+tm_shape(tract,name = 'X2018_Prope') + tm_polygons('X2018_Prope')+tm_shape(tract,name = 'X2018_Rape') + tm_polygons('X2018_Rape')
map_tract

library(car)
full.model <- lm(sub_df$X2018_Murde.1 ~sub_df$Mean_NTL + sub_df$Pop_Dens_1 + sub_df$Employ_R_1 + sub_df$Poverty__1 + sub_df$Educatio_1 + sub_df$Median_I_1 + sub_df$House_De_1 + sub_df$Vacant_R_1 + sub_df$House_Va_1 + sub_df$Room_Num_1)
reduced.model <- step(full.model, direction = 'backward')

Start:  AIC=1396.79
sub_df$X2018_Murde.1 ~ sub_df$Mean_NTL + sub_df$Pop_Dens_1 + 
    sub_df$Employ_R_1 + sub_df$Poverty__1 + sub_df$Educatio_1 + 
    sub_df$Median_I_1 + sub_df$House_De_1 + sub_df$Vacant_R_1 + 
    sub_df$House_Va_1 + sub_df$Room_Num_1

                    Df Sum of Sq     RSS    AIC
- sub_df$House_De_1  1      4649 1605966 1395.2
- sub_df$House_Va_1  1      8349 1609665 1395.6
<none>                           1601316 1396.8
- sub_df$Vacant_R_1  1     36966 1638282 1398.2
- sub_df$Poverty__1  1     50366 1651682 1399.4
- sub_df$Employ_R_1  1    103504 1704820 1404.1
- sub_df$Pop_Dens_1  1    110474 1711791 1404.7
- sub_df$Educatio_1  1    150665 1751982 1408.1
- sub_df$Median_I_1  1    186520 1787836 1411.1
- sub_df$Room_Num_1  1    247568 1848884 1416.1
- sub_df$Mean_NTL    1    533447 2134763 1437.3

Step:  AIC=1395.22
sub_df$X2018_Murde.1 ~ sub_df$Mean_NTL + sub_df$Pop_Dens_1 + 
    sub_df$Employ_R_1 + sub_df$Poverty__1 + sub_df$Educatio_1 + 
    sub_df$Median_I_1 + sub_df$Vacant_R_1 + sub_df$House_Va_1 + 
    sub_df$Room_Num_1

                    Df Sum of Sq     RSS    AIC
- sub_df$House_Va_1  1     12100 1618066 1394.3
<none>                           1605966 1395.2
- sub_df$Vacant_R_1  1     32868 1638834 1396.2
- sub_df$Poverty__1  1     46712 1652678 1397.5
- sub_df$Employ_R_1  1    103232 1709197 1402.4
- sub_df$Pop_Dens_1  1    140202 1746168 1405.6
- sub_df$Educatio_1  1    151193 1757158 1406.5
- sub_df$Median_I_1  1    192285 1798251 1410.0
- sub_df$Room_Num_1  1    243340 1849305 1414.1
- sub_df$Mean_NTL    1    565638 2171603 1437.9

Step:  AIC=1394.33
sub_df$X2018_Murde.1 ~ sub_df$Mean_NTL + sub_df$Pop_Dens_1 + 
    sub_df$Employ_R_1 + sub_df$Poverty__1 + sub_df$Educatio_1 + 
    sub_df$Median_I_1 + sub_df$Vacant_R_1 + sub_df$Room_Num_1

                    Df Sum of Sq     RSS    AIC
<none>                           1618066 1394.3
- sub_df$Vacant_R_1  1     35808 1653874 1395.6
- sub_df$Poverty__1  1     40946 1659012 1396.0
- sub_df$Employ_R_1  1     94801 1712867 1400.8
- sub_df$Pop_Dens_1  1    135438 1753504 1404.2
- sub_df$Educatio_1  1    141551 1759617 1404.7
- sub_df$Median_I_1  1    221933 1839999 1411.3
- sub_df$Room_Num_1  1    251779 1869845 1413.7
- sub_df$Mean_NTL    1    748308 2366374 1448.6

reduced.model

Call:
lm(formula = sub_df$X2018_Murde.1 ~ sub_df$Mean_NTL + sub_df$Pop_Dens_1 + 
    sub_df$Employ_R_1 + sub_df$Poverty__1 + sub_df$Educatio_1 + 
    sub_df$Median_I_1 + sub_df$Vacant_R_1 + sub_df$Room_Num_1)

Coefficients:
      (Intercept)    sub_df$Mean_NTL  sub_df$Pop_Dens_1  sub_df$Employ_R_1  
       -296.00388            4.12917           -0.53812           50.41677  
sub_df$Poverty__1  sub_df$Educatio_1  sub_df$Median_I_1  sub_df$Vacant_R_1  
        -11.31568          -39.52070           -0.00346          327.33486  
sub_df$Room_Num_1  
         87.60889